机器学习算法在紧急医疗服务中的应用和性能:一个范围审查
Ahmad Alrawashdeh1, Saeed Alqahtani2, Zaid I Alkhatib1
1Department of Allied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan.
Prehospital and disaster medicine
|May 17, 2024
概括
机器学习 (ML) 算法在改善紧急医疗服务 (EMS) 临床和操作性能方面表现有前途. 未来的研究应该针对提高ML模型精度的特定条件.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 紧急医疗研究 紧急医疗研究
背景情况:
- 紧急医疗服务 (EMS) 在优化临床和操作性能方面面临挑战.
- 机器学习 (ML) 为提高EMS效率和患者护理提供了潜在的解决方案.
研究的目的:
- 系统地审查和总结有关EMS内的ML应用的文献.
- 评估ML算法在临床和操作EMS领域的性能.
主要方法:
- 在四个电子数据库 (从开始到2024年1月) 进行了全面的文献搜索.
- 使用ML算法进行EMS增强的原始研究被选,并由两个独立审查员提取数据.
- 编制了研究特征的定量描述,ML算法和性能指标.
主要成果:
- 该审查包括164项研究 (2005-2024年),其中125项侧重于临床方面,39项侧重于操作方面.
- 临床ML应用包括分拣,诊断和结果预测,特别是医院外心脏骤停,中位数AUC为85.6%.
- 运营性ML应用专注于救护车分配和路线优化,达到96.1%的中位数AUC;神经网络和整体算法通常表现最好.
结论:
- ML算法可以显著改善医院前医疗状况管理和救护车性能.
- 未来的研究应该集中在特定的临床条件或操作任务上,以完善ML模型的性能指标.
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